OpenRaiser/Intern-Atlas huggingface dataset:
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
Venue: arXiv cs.AI 2023
Year: 2023

Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. Artificial intelligence technology has emerged as a promising solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction and delivering personalized feedback. Hence, by collecting health datasets from 321 adolescents, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. The proposed system shows significant potential in effectively addressing childhood and adolescent obesity.
Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis
Venue: arXiv cs.CV 2023
Year: 2023

BACKGROUND & AIMS: Histological remission (HR) is advocated and considered as a new therapeutic target in ulcerative colitis (UC). Diagnosis of histologic remission currently relies on biopsy; during this process, patients are at risk for bleeding, infection, and post-biopsy fibrosis. In addition, histologic response scoring is complex and time-consuming, and there is heterogeneity among pathologists. Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique that can provide excellent in vivo assessment of glands. Based on the EC technique, we propose a neural network model that can assess histological disease activity in UC using EC images to address the above issues. The experiment results demonstrate that the proposed method can assist patients in precise treatment and prognostic assessment. METHODS: We construct a neural network model for UC evaluation. A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score. Subsequently, 103 intestinal segments are used as the training set, 16 intestinal segments are used as the validation set for neural network training, and the remaining 35 intestinal segments are used as the test set to measure the model performance together with the validation set. RESULTS: By treating HR as a negative category and histologic activity as a positive category, the proposed neural network model can achieve an accuracy of 0.9, a specificity of 0.95, a sensitivity of 0.75, and an area under the curve (AUC) of 0.81. CONCLUSION: We develop a specific neural network model that can distinguish histologic remission/activity in EC images of UC, which helps to accelerate clinical histological diagnosis. keywords: ulcerative colitis; Endocytoscopy; Geboes score; neural network.
Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts
Venue: arXiv cs.CL 2023
Year: 2023

Detecting online sexual predatory behaviours and abusive language on social media platforms has become a critical area of research due to the growing concerns about online safety, especially for vulnerable populations such as children and adolescents. Researchers have been exploring various techniques and approaches to develop effective detection systems that can identify and mitigate these risks. Recent development of large language models (LLMs) has opened a new opportunity to address this problem more effectively. This paper proposes an approach to detection of online sexual predatory chats and abusive language using the open-source pretrained Llama 2 7B-parameter model, recently released by Meta GenAI. We fine-tune the LLM using datasets with different sizes, imbalance degrees, and languages (i.e., English, Roman Urdu and Urdu). Based on the power of LLMs, our approach is generic and automated without a manual search for a synergy between feature extraction and classifier design steps like conventional methods in this domain. Experimental results show a strong performance of the proposed approach, which performs proficiently and consistently across three distinct datasets with five sets of experiments. This study's outcomes indicate that the proposed method can be implemented in real-world applications (even with non-English languages) for flagging sexual predators, offensive or toxic content, hate speech, and discriminatory language in online discussions and comments to maintain respectful internet or digital communities. Furthermore, it can be employed for solving text classification problems with other potential applications such as sentiment analysis, spam and phishing detection, sorting legal documents, fake news detection, language identification, user intent recognition, text-based product categorization, medical record analysis, and resume screening.
Assessing Trust in Construction AI-Powered Collaborative Robots using Structural Equation Modeling
Venue: arXiv cs.HC 2023
Year: 2023

This study aimed to investigate the key technical and psychological factors that impact the architecture, engineering, and construction (AEC) professionals' trust in collaborative robots (cobots) powered by artificial intelligence (AI). The study employed a nationwide survey of 600 AEC industry practitioners to gather in-depth responses and valuable insights into the future opportunities for promoting the adoption, cultivation, and training of a skilled workforce to leverage this technology effectively. A Structural Equation Modeling (SEM) analysis revealed that safety and reliability are significant factors for the adoption of AI-powered cobots in construction. Fear of being replaced resulting from the use of cobots can have a substantial effect on the mental health of the affected workers. A lower error rate in jobs involving cobots, safety measurements, and security of data collected by cobots from jobsites significantly impact reliability, while the transparency of cobots' inner workings can benefit accuracy, robustness, security, privacy, and communication, and results in higher levels of automation, all of which demonstrated as contributors to trust. The study's findings provide critical insights into the perceptions and experiences of AEC professionals towards adoption of cobots in construction and help project teams determine the adoption approach that aligns with the company's goals workers' welfare.
Heterogeneous Drone Small Cells: Optimal 3D Placement for Downlink Power Efficiency and Rate Satisfaction
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we consider a heterogeneous repository of drone-enabled aerial base stations with varying transmit powers that provide downlink wireless coverage for ground users. One particular challenge is optimal selection and deployment of a subset of available drone base stations (DBSs) to satisfy the downlink data rate requirements while minimizing the overall power consumption. In order to address this challenge, we formulate an optimization problem to select the best subset of available DBSs so as to guarantee wireless coverage with some acceptable transmission rate in the downlink path. In addition to the selection of DBSs, we determine their 3D position so as to minimize their overall power consumption. Moreover, assuming that the DBSs operate in the same frequency band, we develop a novel and computationally efficient beamforming method to alleviate the inter-cell interference impact on the downlink. We propose a Kalai-Smorodinsky bargaining solution to determine the optimal beamforming strategy in the downlink path to compensate for the impairment caused by the interference. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the heterogeneous drone-based small cell networks.
Bayesian artificial brain with ChatGPT
Venue: arXiv cs.AI 2023
Year: 2023

This paper aims to investigate the mathematical problem-solving capabilities of Chat Generative Pre-Trained Transformer (ChatGPT) in case of Bayesian reasoning. The study draws inspiration from Zhu & Gigerenzer's research in 2006, which posed the question: Can children reason the Bayesian way? In the pursuit of answering this question, a set of 10 Bayesian reasoning problems were presented. The results of their work revealed that children's ability to reason effectively using Bayesian principles is contingent upon a well-structured information representation. In this paper, we present the same set of 10 Bayesian reasoning problems to ChatGPT. Remarkably, the results demonstrate that ChatGPT provides the right solutions to all problems.
Online Poker and Rummy -- Games of Skill or Chance?
Venue: arXiv cs.HC 2023
Year: 2023

The paper aims to investigate the degree of cognitive skills required for success in online versions of the popular card game rummy and poker. The study focuses on analyzing the impact of experience and learnable skills on success in the online card game. We also propose a framework to analyze online games to conclude on whether they are games of learnable skill or are they games of chance. The hypotheses proposed aim to test whether online and offline card games are comparable in terms of cognitive engagement and skill requirements. To assess these hypotheses, key elements of gameplay such as shuffling of cards, card deck randomness, and seating of players are analyzed. We also adopted statistical approaches to understand the characteristics of card games in terms of random chance or skill. From the analysis, we could see that the normality of the derived variables deviates significantly from the normal distribution showing a non-linear trend. It signifies that the mean of the involved skill variables is not zero as the user plays a greater number of games, thereby strengthening the assumption that the long-term success in online card games is attributed to skill and not chance. There is no difference in online and offline versions of card games (rummy and poker) from the perspective of requirement of skills. Moreover, our finding suggests that there is a preponderance of skills to succeed in online card gaming. Overall, the findings of this research contribute to a better understanding of cognitive skills in online gaming environments.
Generating tabular datasets under differential privacy
Venue: arXiv cs.LG 2023
Year: 2023

Machine Learning (ML) is accelerating progress across fields and industries, but relies on accessible and high-quality training data. Some of the most important datasets are found in biomedical and financial domains in the form of spreadsheets and relational databases. But this tabular data is often sensitive in nature. Synthetic data generation offers the potential to unlock sensitive data, but generative models tend to memorise and regurgitate training data, which undermines the privacy goal. To remedy this, researchers have incorporated the mathematical framework of Differential Privacy (DP) into the training process of deep neural networks. But this creates a trade-off between the quality and privacy of the resulting data. Generative Adversarial Networks (GANs) are the dominant paradigm for synthesising tabular data under DP, but suffer from unstable adversarial training and mode collapse, which are exacerbated by the privacy constraints and challenging tabular data modality. This work optimises the quality-privacy trade-off of generative models, producing higher quality tabular datasets with the same privacy guarantees. We implement novel end-to-end models that leverage attention mechanisms to learn reversible tabular representations. We also introduce TableDiffusion, the first differentially-private diffusion model for tabular data synthesis. Our experiments show that TableDiffusion produces higher-fidelity synthetic datasets, avoids the mode collapse problem, and achieves state-of-the-art performance on privatised tabular data synthesis. By implementing TableDiffusion to predict the added noise, we enabled it to bypass the challenges of reconstructing mixed-type tabular data. Overall, the diffusion paradigm proves vastly more data and privacy efficient than the adversarial paradigm, due to augmented re-use of each data batch and a smoother iterative training process.
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
Venue: arXiv cs.LG 2023
Year: 2023

Continual learning (CL) refers to the ability of an intelligent system to sequentially acquire and retain knowledge from a stream of data with as little computational overhead as possible. To this end; regularization, replay, architecture, and parameter isolation approaches were introduced to the literature. Parameter isolation using a sparse network which enables to allocate distinct parts of the neural network to different tasks and also allows to share of parameters between tasks if they are similar. Dynamic Sparse Training (DST) is a prominent way to find these sparse networks and isolate them for each task. This paper is the first empirical study investigating the effect of different DST components under the CL paradigm to fill a critical research gap and shed light on the optimal configuration of DST for CL if it exists. Therefore, we perform a comprehensive study in which we investigate various DST components to find the best topology per task on well-known CIFAR100 and miniImageNet benchmarks in a task-incremental CL setup since our primary focus is to evaluate the performance of various DST criteria, rather than the process of mask selection. We found that, at a low sparsity level, Erdos-Rényi Kernel (ERK) initialization utilizes the backbone more efficiently and allows to effectively learn increments of tasks. At a high sparsity level, unless it is extreme, uniform initialization demonstrates a more reliable and robust performance. In terms of growth strategy; performance is dependent on the defined initialization strategy and the extent of sparsity. Finally, adaptivity within DST components is a promising way for better continual learners.
Identifying and Mitigating the Security Risks of Generative AI
Venue: arXiv cs.AI 2023
Year: 2023

Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive, but is rather an attempt to synthesize some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides both a launching point for a discussion on this important topic as well as interesting problems that the research community can work to address.
Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction
Venue: arXiv cs.HC 2023
Year: 2023

Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR experiences. While VR/AR head-mounted displays unlock users' natural wide-range head movements during viewing, their neck muscle comfort is inevitably compromised by the added hardware weight. Unfortunately, little quantitative knowledge for understanding and addressing such an issue is available so far. Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment. Specifically, by learning from collected physiological data, we develop a bio-physically inspired computational model to predict neck MCL under diverse head kinematic states. Beyond quantifying the cumulative MCL of completed head movements, our model can also predict potential MCL requirements with target head poses only. A series of objective evaluations and user studies demonstrate its prediction accuracy and generality, as well as its ability in reducing users' neck discomfort by optimizing the layout of visual targets. We hope this research will motivate new ergonomic-centered designs for VR/AR and interactive graphics applications. Source code is released at: https://github.com/NYU-ICL/xr-ergonomics-neck-comfort.
Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models
Venue: arXiv cs.CL 2023
Year: 2023

This report introduces the Attention Visualizer package, which is crafted to visually illustrate the significance of individual words in encoder-only transformer-based models. In contrast to other methods that center on tokens and self-attention scores, our approach will examine the words and their impact on the final embedding representation. Libraries like this play a crucial role in enhancing the interpretability and explainability of neural networks. They offer the opportunity to illuminate their internal mechanisms, providing a better understanding of how they operate and can be enhanced. You can access the code and review examples on the following GitHub repository: https://github.com/AlaFalaki/AttentionVisualizer.
Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software Systems with the Brazilian General Data Protection Law
Venue: arXiv cs.SE 2023
Year: 2023

Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate.
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment. Recent work has shown that offline reinforcement learning can be formulated as a sequence modeling problem and solved via supervised learning with approaches such as decision transformer. While these sequence-based methods achieve competitive results over return-to-go methods, especially on tasks that require longer episodes or with scarce rewards, importance sampling is not considered to correct the policy bias when dealing with off-policy data, mainly due to the absence of behavior policy and the use of deterministic evaluation policies. To this end, we propose DPE: an RL algorithm that blends offline sequence modeling and offline reinforcement learning with Double Policy Estimation (DPE) in a unified framework with statistically proven properties on variance reduction. We validate our method in multiple tasks of OpenAI Gym with D4RL benchmarks. Our method brings a performance improvements on selected methods which outperforms SOTA baselines in several tasks, demonstrating the advantages of enabling double policy estimation for sequence-modeled reinforcement learning.
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning
Venue: arXiv cs.CV 2023
Year: 2023

Object-centric representation learning offers the potential to overcome limitations of image-level representations by explicitly parsing image scenes into their constituent components. While image-level representations typically lack robustness to natural image corruptions, the robustness of object-centric methods remains largely untested. To address this gap, we present the RobustCLEVR benchmark dataset and evaluation framework. Our framework takes a novel approach to evaluating robustness by enabling the specification of causal dependencies in the image generation process grounded in expert knowledge and capable of producing a wide range of image corruptions unattainable in existing robustness evaluations. Using our framework, we define several causal models of the image corruption process which explicitly encode assumptions about the causal relationships and distributions of each corruption type. We generate dataset variants for each causal model on which we evaluate state-of-the-art object-centric methods. Overall, we find that object-centric methods are not inherently robust to image corruptions. Our causal evaluation approach exposes model sensitivities not observed using conventional evaluation processes, yielding greater insight into robustness differences across algorithms. Lastly, while conventional robustness evaluations view corruptions as out-of-distribution, we use our causal framework to show that even training on in-distribution image corruptions does not guarantee increased model robustness. This work provides a step towards more concrete and substantiated understanding of model performance and deterioration under complex corruption processes of the real-world.